Health care providers rely on an understanding of joint anatomy and mechanics when evaluating a subject's suspected joint problem and/or biomechanical performance issue. Understanding anatomy and joint biomechanics assists in the diagnosis and evaluation of a subject for an orthopedic intervention. However, currently available diagnostic tools are limited in the level of detail and analysis that can be achieved. Typically, when treating joint problems, the intention is to address a specific structural or mechanical problem within the joint. For example, a surgeon might prescribe a specific procedure to correct the joint alignment problem, or a physical therapist might prescribe exercises to strengthen a specific tendon or muscle that is responsible for a joint problem, etc.
It follows, therefore, that the extent to which a specific treatable joint defect can be identified and optimally treated directly impacts the success of any treatment protocol. Currently available orthopedic diagnostic methods are capable of detecting a limited number of specific and treatable defects. These techniques include X-Rays, MRI, discography, and physical exams of the patient. These methods have become widely available and broadly adopted into the practice of treating joint problems and addressing joint performance issues. However, currently available diagnostic techniques provide measurement data that is imprecise and often inconclusive which results in an inability to detect many types of pathologies or to accurately assess pathologies that might be considered borderline. As a result, a significant number of patients having joint problems remain undiagnosed and untreated using current techniques, or worse are misdiagnosed and mistreated due to the poor clinical efficacy of these techniques.
There are currently no reliable techniques for identifying soft tissue injury. Muscle guarding is a well established concept that is hypothesized to be highly prevalent among sufferers of joint pain. In muscle guarding, a subject responds to chronic pain by immobilizing the painful area through involuntary muscle involvement. The ability to isolate different muscle groups is desirable to determine which muscle group or combination of groups, if any, could be contributing to, or responsible for, any joint dysfunction.
Additionally, the level of entrenchment of muscle guarding behavior cannot currently be determined. With respect to treatment decisions, the operative question in determining the level of “entrenchment” of any observed muscle guarding is to determine if the muscle guarding behavior is one which conservative methods of therapy could address through non-surgical therapy, or alternatively determining that the muscle guarding behavior so “entrenched” that such efforts would be futile and surgery should be considered.
Further, assuming that a system of measuring the surface motion of joints and the motion between internal joint structures that accounts for various types of muscle involvements would be possible, there would be a need for investigational data from controlled clinical trials to be collected across a broad population of subjects to afford for comparative analyses between subjects. Such a comparative analysis across a broad population of subjects would be necessary for the purpose of defining “normal” and “unhealthy” ranges of such measurements, which would in turn form the basis for the diagnostic interpretation of such measurements.
There have been significant technological innovations to the field of orthopedic interventions over the last few decades, specifically with the use of prosthetic and therapeutic devices to correct mechanical and structural defects of the bones and joints and to restore proper joint function. There have also been significant advances in the application of chiropractic and physical therapy approaches to correct muscle-, ligament-, and tendon-related defects. There has not however, been a corresponding improvement in the diagnostic methods used to identify proper candidates for these interventions. As a result, the potential impact and utility of the improvements in orthopedic intervention has been limited.
Imaging is the cornerstone of all modern orthopedic diagnostics. The vast majority of diagnostic performance innovations have focused on static images. Static images are a small number of images of a joint structure taken at different points in the joint's range of motion, with the subject remaining still in each position while the image is being captured. Static imaging studies have focused mainly on detecting structural changes to the bones and other internal joint structures. An example of the diagnostic application of static imaging studies is with the detection of spinal disc degeneration by the use of plain X-rays, and MR images. However, these applications yield poor diagnostic performance with an unacceptably high proportion of testing events yielding either inconclusive or false positive/false negative diagnostic results (Lawrence, J. S. (1969) Annals of Rheumatic Diseases 28: 121-37; Waddell, G. (1998) The Back Pain Revolution. Edinburgh, Churchill Livingstone Ch 2 p 22; Carragee et al. (2006) Spine 31(5): 505-509, McGregor et al. (1998) J Bone Joint Surg (Br) 80-B: 1009-1013; Fujiwara et al. (2000(a)) Journal of Spinal Disorders 13: 444-50).
Cine-radiography of uncontrolled weightbearing motion (Harada et al. (2000) Spine 25: 1932-7); Takavanagi et al. (2001) Spine 26(17): 1858-1865) has been used to provide a set of static images to which digital markers have been attached and transformed to give quantitative measurement of joint motion. Similar measurement of joint motion has been achieved using videofluoroscopy (Breen et al. (1989) Journal of Biomedical engineering 11: 224-8; Cholewicki et al. (1991) Clinical Biomechanics 6: 73-8; Breen et al. (1993) European Journal of Physical Medicine and Rehabilitation 3(5): 182-90; Brydges et al. 1993). This method has also been used to study the effects on joint motion of weightlifting (Cholewicki et al. (1992) Journal of Biomechanics 25(1): 17-28). Prior procedures using this method involve using a manual process in which internal joint structures are marked by hand with digital landmarks on digital image files of consecutive frames of videoflouroscopy recordings of a subject's joint motion. A computer then automatically determines the frame-to-frame displacement between such digital landmarks to derive quantitative measurements of the motion of joint structures (Lee et al. (2002) Spine 27(8): E215-20). Even more recently, this approach has been accomplished using an automatic registration process (Wong et al. (2006) Spine 31(4): 414-19) that eliminates the manual marking process and thus reduces the laboriousness of the previous processes. However both of these methods, as well as all of the other methods mentioned in this paragraph, studied the motion of joints based on the imaging of uncontrolled, weightbearing body motion.
Using uncontrolled, weightbearing motion to derive quantitative measurements of joint motion confounds the diagnostic interpretation of such measurements so as to render them diagnostically useless. The diagnostic interpretation of such measurements would normally be based on a comparative analysis of joint motion measurements across a wide population of subjects, and would strive to identify statistically significant differences in these measurements between “normal” and “unhealthy” subjects, such that any given subject can be classified as “normal” or “unhealthy” based on that subject's joint motion measurement values. For such purposes, it is necessary to reduce the background variability of measurements across tested subjects as much as possible, so that any observed difference between “normal” and “unhealthy” subjects can be definitively attributable to a specific condition. Not controlling the motion that is being studied introduces variability into these comparative analyses due to differences that exist across testing subjects with respect to each subject's individual range of motion, symmetry of motion, and regularity of motion. These differences affect the joint motion of each subject differently, and collectively serve to create wide variability among joint motion measurements across subjects. Controlling for these factors by ensuring a consistent, regular, and symmetric body part motion during diagnostic testing serves to minimize the effects of these factors on a subject's relevant joint motion measurements, thereby reducing the variability of such measurements across subjects and therefore increasing the likelihood that such measurements will yield useful diagnostic results.
Purely qualitative methods for visualizing joint motion have been available for some time using cine-radiography (Jones, M. D. (1962) Archives of Surgery 85: 974-81). More recently, computer edge extraction of vertebral images from fluoroscopy has been used to improve this visualization for use in animations (Zheng et al. (2003) Medical Engineering and Physics 25: 171-179). These references do not, however, provide for any form of measurement or identification of objectively defined motion abnormalities, and therefore is of very limited diagnostic value other than in the detection of grossly and visibly obvious abnormalities that would be detectable using static image analysis methods. Without any quantitative or objective measurement parameters defined, it is impossible to utilize such approaches in comparative analyses across wide populations of subjects, which is required for the purpose of the producing definitive diagnostic interpretations of the results as being either “normal” or “unhealthy”. Further, there have been no diagnostically useful validations of qualitative motion patterns that are generally absent in non-sufferers but present in subjects suffering from known and specific joint functional derangements or symptoms, or vice versa.
Prior attempts at controlling the motion to derive quantitative measurements have provided a more accurate measurement of joint motion, however, some aspects of the motion have continued to be uncontrolled. For example, while some researchers have been able to control the gross motion of the subject, they have not been able to control for the rotation of the patient at their hips while undergoing the controlled motion sweep. This uncontrolled rotation during the controlled sweep of trunk bending confounds the goal of controlling the motion, but also can confound the quantitative imaging results. When the subject rotates out of plane during the trunk bending, the definition of the vertebra in the image becomes compromised, making the vertebra harder to track throughout the image sequence, and thus confounding the diagnostic measurement results.
Methods with living subjects have been able to obtain a high degree of accuracy in measuring the motion of internal joint structures by placing internal markers on the bones of subjects and digitally marking sets of static images (Johnsson et al. (1990) Spine 15: 347-50), a technique known as roentgen stereophotogrammetry analysis (RSA). However RSA requires the surgical implantation of these markers into subjects' internal joint structures, requires the use of two radiographic units simultaneously, and requires a highly complicated calibration process for every single test, and therefore is too invasive and too cumbersome a process for practicable clinical application.
A method for determining vertebral body positions using skin markers was developed (Bryant (1989) Spine 14(3): 258-65) but could only measure joint motion at skin positions and could not measure the motion of structures within the joint. There have been many examples skin marker based spine motion measurement that have all been similarly flawed.
Methods have been developed to measure changes to the position of vertebrae under different loads in dead subjects, whose removed spines were fused and had markers inserted into the vertebrae (Esses et al. (1996) Spine 21(6): 676-84). The motion of these markers was then measured in the presence of different kinds of loads on the vertebrae. This method is, however, inherently impractical for clinical diagnostic use. Other methods with living subjects have been able to obtain a high degree of accuracy in measuring the motion of internal joint structures by placing internal markers on the bones of subjects and digitally marking sets of static images (Johnsson et al. (1990) Spine 15: 347-50), a technique known as roentgen stereophotogrammetry analysis (RSA). However RSA requires the surgical implantation of these markers into subjects' internal joint structures, requires the use of two radiographic units simultaneously, and requires a highly complicated calibration process for every single test, and therefore is too invasive and too cumbersome a process for practicable clinical application.
In addition to failing to control motion during testing, not accounting for the involvement and effects of muscles that are acting when a subject moves under their own muscular force while in a weight-bearing stance further adds to this variability by introducing such inherently variable factors such as the subject's muscle strength, level of pain, involuntary contraction of opposing muscle groups, and neuro-muscular co-ordination. Taken together, all of these sources of variability serve to confound diagnostic conclusions based on comparative analyses by making the ranges of “normal” and those of “abnormal” difficult to distinguish from one another other in a statistically significant way. Such an inability to distinguish between “normal” and “unhealthy” subjects based on a specific diagnostic measurement renders such a measurement diagnostically useless, as has been the case heretofore known which has focused on measurements of uncontrolled joint motion measured in subjects in weight-bearing postures and moving their joints through the power of their own muscles and in an uncontrolled fashion.
U.S. Pat. No. 5,505,208 to Toomin et al. developed a method for measuring muscle dysfunction by collecting muscle activity measurements using electrodes in a pattern across a subject's back while having the subject perform a series of poses where measurements are made at static periods within the movement. These electromyographical readings of “unhealthy” subjects were then compared to those of a “normal” population so as to be able to identify those subjects with abnormal readings, however does not provide for a method to report the results as a degree of departure from an ideal reading, instead can only say whether the reading is “abnormal”. U.S. Pat. No. 6,280,395 to Appel et al. added an additional advantage to this method for determining muscle dysfunction by using the same method, yet adding the ability to better normalize the data by employing a more accurate reading of the thickness of the adipose tissue and other general characteristics that might introduce variability into the readings, as well as the ability to quantify how abnormal a subject's electromyographical reading is as compared to a “normal” population.
Electromyographic measurements taken during weight-bearing joint motion, with simultaneous recording of the motion of the body part using goniometers and also with simultaneous recordings of the motion of internal joint structures through the tracking of surgically-implanted metal markers, has been used to correlate muscle activity with the motion of joints and internal joint structures (Kaigle, supra). However this approach studied joint motion that was uncontrolled and required an invasive surgical procedure to place the metal markers, and thus were neither useful nor feasible for clinical diagnostic application.
Electromyography has also been used in conjunction with a device that provides transient force perturbation so as to observe whether there is a difference between subjects with low back pain and those without low back pain to determine how their muscles respond to such a force. (Stokes, Fox et al. 2006) The objective was to determine whether there is an altered muscle activation pattern when using a ramped effort. This approach however does not address the issue of which discrete muscle group or groups might account for the difference between activation patterns in subjects with joint dysfunctions and those without. Furthermore, this method does not take into consideration the internal structural joint motions and thus provides an incomplete set of information upon which to draw diagnostic conclusions.
What is therefore needed is an apparatus and process for using the apparatus that solves the previously-identified issues, thus providing the clinician and medical device researcher with valuable diagnostic data.